AI 中文总结
该研究将严重裁剪视为抗擦除问题，提出CREST框架，结合编码冗余与神经嵌入恢复，在严重裁剪下大幅提升消息恢复性能，优于现有基线。
AI 中文摘要
图像中的鲁棒消息嵌入对于版权保护和内容追踪等多媒体安全应用至关重要。现有方法大多在失真鲁棒性范式下开发，其中嵌入的信号在空间上仍然存在，但会被噪声、模糊或压缩所劣化。严重裁剪带来了根本不同的挑战，因为它会移除载体本身的一部分，导致有效载荷部分消失，而非仅仅是信号损坏。在本文中，我们从抗擦除的角度重新审视鲁棒消息嵌入，并提出了CREST，一个用于严重裁剪鲁棒嵌入的概念验证框架。CREST将编码理论冗余与神经嵌入和恢复相结合，通过LT喷泉编码将紧凑的QR消息扩展为冗余空间有效载荷，并将其与感知裁剪的嵌入和片段恢复相结合。在COCO、DIV2K和VOC2012上的实验表明，CREST在严重裁剪下的恢复能力得到提升，同时保持了有竞争力的视觉质量。在面积保留率为0.7的混合失真下，CREST将TRA从18.52%提升至68.45%，并将EMR从13.88%降低至4.21%，优于最强基线。在COCO2017上，当仅保留30%至50%的图像面积时，CREST仍能实现48.55%至65.12%的TRA，而所有对比基线均无法恢复消息。这些结果表明，严重裁剪更适合被理解为一个擦除问题，而非传统的失真问题，这为神经嵌入与基于编码的恢复的联合设计提供了动机。
英文摘要
Robust message embedding in images is important for multimedia security applications such as copyright protection and content tracing. Existing methods are largely developed under a distortion robustness paradigm, where the embedded signal remains spatially present but is degraded by noise, blur, or compression. Severe cropping poses a fundamentally different challenge because it removes part of the carrier itself, causing partial payload disappearance rather than mere signal corruption. In this paper, we revisit robust message embedding from an erasure-resilience perspective and present CREST, a proof-of-concept framework for severe-cropping-robust embedding. CREST combines coding-theoretic redundancy with neural embedding and recovery by expanding a compact QR message into a redundant spatial payload via LT fountain coding and coupling it with cropping-aware embedding and fragment recovery. Experiments on COCO, DIV2K, and VOC2012 show that CREST improves recovery under severe cropping while maintaining competitive visual quality. Under mixed distortions with an area retention ratio of 0.7, CREST improves TRA from 18.52% to 68.45% and reduces EMR from 13.88% to 4.21% over the strongest baseline. On COCO2017, CREST still achieves 48.55--65.12% TRA when only 30--50% of the image area is retained, whereas all compared baselines fail to recover the message. These results suggest that severe cropping is better understood as an erasure problem rather than a conventional distortion problem, motivating the joint design of neural embedding and coding-based recovery.
CommentsAccepted by ACMMM 2026
